steven_li
33a9845566
feat(engine): 添加技能查看工具并优化异步任务管理 - 添加SkillViewTool到引擎加载器中，增强技能管理功能 - 在AgentLoop中引入_active_direct_task来跟踪活跃任务 - 实现直接任务执行时的同步处理逻辑 - 更新工具实例化方式以支持依赖注入 feat(config): 增加智能体运行时参数配置支持 - 扩展AgentDefaultsConfig添加max_tokens和temperature字段 - 实现配置解析函数_first_config_value处理多个配置源 - 支持通过Web API动态更新智能体运行时参数 - 添加前端页面配置表单和验证逻辑 refactor(provider): 统一最大令牌数参数类型为可选整型 - 将所有LLM提供者的max_tokens参数改为int | None类型 - 为AnthropicProvider实现模型特定的最大令牌数默认值 - 调整参数传递逻辑，优先级：调用参数 > 配置文件 > 模型默认值 - 移除硬编码的默认值，改用条件判断 feat(process): 增强事件投影功能 - 添加工具调用开始/结束事件的映射逻辑 - 实现技能激活事件的识别和展示 - 添加辅助函数处理工具调用名称和参数提取 - 优化运行记录关联逻辑，提升事件匹配准确性 fix(web): 更新网络请求客户端信任环境设置 - 将WebFetchTool和WebSearchTool的trust_env参数设为True - 确保HTTP客户端能够正确使用系统代理配置 - 修复可能的网络连接问题 test: 添加配置加载和事件投影相关测试 - 新增智能体默认参数配置测试用例 - 实现API配置持久化和重载测试 - 添加技能卡片和工具事件的投影测试 ```
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
"""Direct OpenAI-compatible provider — bypasses LiteLLM."""
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from __future__ import annotations
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from typing import Any
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from .base import LLMProvider, LLMResponse, ToolCallRequest
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try: # pragma: no cover - optional dependency
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import json_repair
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except ModuleNotFoundError: # pragma: no cover
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json_repair = None # type: ignore[assignment]
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try: # pragma: no cover - optional dependency
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from openai import AsyncOpenAI
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except ModuleNotFoundError: # pragma: no cover
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AsyncOpenAI = None # type: ignore[assignment]
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class CustomProvider(LLMProvider):
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"""直接连接任意 OpenAI-compatible endpoint。"""
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def __init__(
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self,
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api_key: str = "no-key",
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api_base: str = "http://localhost:8000/v1",
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default_model: str = "default",
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request_timeout_seconds: float | None = None,
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) -> None:
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super().__init__(api_key, api_base, request_timeout_seconds=request_timeout_seconds)
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self.default_model = default_model
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self._client = None
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def _client_or_raise(self):
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if AsyncOpenAI is None:
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raise RuntimeError("openai package is not installed")
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if self._client is None:
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self._client = AsyncOpenAI(
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api_key=self.api_key,
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base_url=self.api_base,
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timeout=self.request_timeout_seconds,
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)
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return self._client
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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temperature: float = 0.7,
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thinking_enabled: bool | None = None,
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) -> LLMResponse:
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client = self._client_or_raise()
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kwargs: dict[str, Any] = {
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"model": model or self.default_model,
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"messages": self.sanitize_empty_content(messages),
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"temperature": temperature,
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}
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if max_tokens is not None:
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kwargs["max_tokens"] = max(1, max_tokens)
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if tools:
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kwargs.update(tools=tools, tool_choice="auto")
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try:
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response = await client.chat.completions.create(**kwargs)
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except Exception as exc:
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return LLMResponse(content=f"Error: {exc}", finish_reason="error", provider_name="custom")
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choice = response.choices[0]
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message = choice.message
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parsed_tool_calls: list[ToolCallRequest] = []
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for tool_call in message.tool_calls or []:
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raw_arguments = tool_call.function.arguments
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if isinstance(raw_arguments, str):
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if json_repair is not None:
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arguments = json_repair.loads(raw_arguments)
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else:
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import json
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arguments = json.loads(raw_arguments)
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else:
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arguments = raw_arguments
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parsed_tool_calls.append(
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ToolCallRequest(
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id=tool_call.id,
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name=tool_call.function.name,
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arguments=arguments,
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)
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)
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usage = getattr(response, "usage", None)
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usage_payload = {}
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if usage is not None:
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usage_payload = {
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"prompt_tokens": getattr(usage, "prompt_tokens", 0),
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"completion_tokens": getattr(usage, "completion_tokens", 0),
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"total_tokens": getattr(usage, "total_tokens", 0),
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}
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return LLMResponse(
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content=message.content,
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tool_calls=parsed_tool_calls,
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finish_reason=choice.finish_reason or "stop",
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usage=usage_payload,
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reasoning_content=getattr(message, "reasoning_content", None),
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provider_name="custom",
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model=model or self.default_model,
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)
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def get_default_model(self) -> str:
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return self.default_model
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